Search Results (203)

Background: Boiling histotripsy (BH) uses high-amplitude, short-pulse focused ultrasound to disrupt tissue mechanically. Oncolytic virotherapy using reovirus has shown modest clinical benefit in pancreatic cancer patients. Here, reovirus and BH were used to treat pancreatic tumours, and their effects on the immune transcriptome of these tumours were characterised. Methods: Orthotopic syngeneic murine pancreatic KPC tumours grown in immune-competent subjects, were allocated to control, reovirus, BH and combined BH and reovirus treatment groups. Acoustic cavitation was monitored using a passive broadband cavitation sensor. Treatment effects were assessed histologically with hematoxylin and eosin staining. Single-cell multi-omics combining whole-transcriptome analysis with the expression of surface-expressed immune proteins was used to assess the effects of treatments on tumoural leukocytes. Results: Acoustic cavitation was detected in all subjects exposed to BH, causing cellular disruption in tumours 6 h after treatment. Distinct cell clusters were identified in the pancreatic tumours 24 h post-treatment. These included neutrophils and cytotoxic T cells overexpressing genes associated with an N2-like and an exhaustion phenotype, respectively. Reovirus decreased macrophages, and BH decreased regulatory T cells compared to controls. The combined treatments increased neutrophils and the ratio of various immune cells to Treg. All treatments overexpressed genes associated with an innate immune response, while ultrasound treatments downregulated genes associated with the transporter associated with antigen processing (TAP) complex. Conclusions: Our results show that the combined BH and reovirus treatments maximise the overexpression of genes associated with the innate immune response compared to that seen with each individual treatment, and illustrate the anti-immune phenotype of key immune cells in the pancreatic tumour microenvironment. Full article

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs. Full article

To enhance the operational stability of distribution networks during peak periods, this paper proposes a multi-timescale operational method considering load forecasting impacts. Firstly, the Crested Porcupine Optimizer (CPO) is employed to optimize the hyperparameters of long short-term memory (LSTM) networks for an accurate prediction of the next-day load curves. Building on this foundation, a multi-timescale optimization strategy is developed: During the day-ahead operation phase, a conservation voltage reduction (CVR)-based regulation plan is formulated to coordinate the control of on-load tap changers (OLTCs) and distributed resources, alleviating peak-shaving pressure on the upstream grid. In the intraday optimization phase, real-time adjustments of OLTC tap positions are implemented to address potential voltage violations, accompanied by an electrical distance-based control strategy for flexible adjustable resources, enabling rapid voltage recovery and enhancing system stability and robustness. Finally, a modified IEEE-33 node system is adopted to verify the effectiveness of the proposed multi-timescale operational method. The method demonstrates a load forecasting accuracy of 93.22%, achieves a reduction of 1.906% in load power demand, and enables timely voltage regulation during intraday limit violations, effectively maintaining grid operational stability. Full article

In contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation into a single framework. We present a multi-task neural network architecture capable of simultaneously estimating channels from multiple base stations in a blind manner while estimating user terminal coordinates in given indoor environments. This approach exploits the relationship between channel characteristics and spatial information, using the same channel state information (CSI) data to perform both tasks with a single model. We evaluate the proposed solution, assessing its effectiveness across differing antenna spacing configurations and indoor test environments using both WiFi and 5G orthogonal frequency-division multiplexing (OFDM) systems. The results show performance benefits, achieving comparable channel estimation results to other studies while simultaneously providing a localization estimate, resulting in reduced model overhead while leveraging spatial context. The presented system demonstrates potential to improve the efficiency of communication systems in real-world applications, aligning with the goals of emerging integrated sensing and communication (ISAC) systems. Results based on experimental data using the proposed solution show a 50th percentile localization error of 1.62 m for 3-tap channels and 0.89 m for 10-tap channels. Full article

Understanding the characteristics of residential hot-water consumption can be useful for developing effective water-conservation strategies in response to increasing pressure on natural resources. This study systematically investigates residential hot-water consumption through direct monitoring of over 40 domestic fixtures (belonging to six different end-use categories) in five Italian households, recorded over a period ranging from approximately two weeks to nearly four months, and using smart meters with 5 min resolution. A multi-step analysis is applied—at both household and end-use levels, explicitly differentiating tap uses by purpose and location—to (i) quantify daily per capita hot-water consumption, (ii) calculate hot-water ratios, and (iii) assess daily profiles. The results show an average total water consumption of 106.7 L/person/day, with at least 26.1% attributed to hot water. In addition, daily profiles reveal distinct patterns across end uses: hot- and cold-water consumption at kitchen sinks are not aligned over time (with cold water peaking before meals and hot water used predominantly afterward), while bathroom taps show more synchronized use and a marked evening peak in hot-water consumption. Study findings—along with the related open-access dataset—provide a valuable benchmark based on field measurements to support in the process of water demand modeling and the development of targeted demand-management strategies. Full article

Traditional Multiple Object Tracking (MOT) methods in satellite videos mostly follow the Detection-Based Tracking (DBT) framework. However, the DBT framework assumes that all objects are correctly recognized and localized by the detector. In practice, the low resolution of satellite videos, small objects, and complex backgrounds inevitably leads to a decline in detector performance. To alleviate the impact of detector degradation on track, we propose Coarse-Fine Tracker, a framework that integrates the MOT framework with the Tracking Any Point (TAP) method CoTracker for the first time, leveraging TAP’s persistent point correspondence modeling to compensate for detector failures. In our Coarse-Fine Tracker, we divide the satellite video into sub-videos. For one sub-video, we first use ByteTrack to track the outputs of the detector, referred to as coarse tracking, which involves the Kalman filter and box-level motion features. Given the small size of objects in satellite videos, we treat each object as a point to be tracked. We then use CoTracker to track the center point of each object, referred to as fine tracking, by calculating the appearance feature similarity between each point and its neighboring points. Finally, the Consensus Fusion Strategy eliminates mismatched detections in coarse tracking results by checking their geometric consistency against fine tracking results and recovers missed objects via linear interpolation or linear fitting. This method is validated on the VISO and SAT-MTB datasets. Experimental results in VISO show that the tracker achieves a multi-object tracking accuracy (MOTA) of 66.9, a multi-object tracking precision (MOTP) of 64.1, and an IDF1 score of 77.8, surpassing the detector-only baseline by 11.1% in MOTA while reducing ID switches by 139. Comparative experiments with ByteTrack demonstrate the robustness of our tracking method when the performance of the detector deteriorates. Full article

Recent advancements in photovoltaic (PV) and battery technologies, combined with improvements in power electronic converters, have accelerated the adoption of rooftop PV systems and electric vehicles (EVs) in distribution networks, while these technologies offer economic and environmental benefits and support the transition to sustainable energy systems, they also introduce operational challenges, including voltage fluctuations, increased system losses, and voltage regulation issues under high penetration levels. Traditional Voltage and Var Control (VVC) strategies, which rely on substation on-load tap changers, voltage regulators, and shunt capacitors, are insufficient to fully manage these challenges. This study proposes a novel Voltage, Var, and Watt Control (VVWC) framework that coordinates the operation of PV and EV resources, conventional devices, and demand responsive loads. A mixed-integer nonlinear multi-objective optimization model is developed, applying a Chebyshev goal programming approach to balance objectives that include minimizing PV curtailment, reducing system losses, flattening voltage profile, and minimizing demand not met. Unserved demand has, in particular, been modeled while incorporating the concepts of distributional and recognition energy justice. The proposed method is validated using a modified version of the IEEE 123-bus test distribution system. The results indicate that the proposed framework allows for high levels of PV and EV integration in the grid, while ensuring that EV demand is met and PV curtailment is negligible. This demonstrates an equitable access to energy, while maximizing renewable energy usage. Full article

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks. Full article

Accurate mechanical fault diagnosis of On-Load Tap Changers (OLTCs) remains crucial for power system reliability yet faces challenges from vibration signals’ non-stationary characteristics and limitations of conventional methods. This paper develops a hybrid framework combining metaheuristic-optimized decomposition with hierarchical temporal learning. The methodology employs a Subtraction-Average-Based Optimizer (SABO) to adaptively configure Time-Varying Filtered Empirical Mode Decomposition (TVFEMD), effectively resolving mode mixing through optimized parameter selection. The decomposed components undergo dual-stage temporal processing: A Temporal Convolutional Network (TCN) extracts multi-scale dependencies via dilated convolution architecture, followed by Gated Recurrent Unit (GRU) layers capturing dynamic temporal patterns. An experimental platform was established using a KM-type OLTC to acquire vibration signals under typical mechanical faults, subsequently constructing the dataset. Experimental validation demonstrates superior classification accuracy compared to conventional decomposition–classification approaches in distinguishing complex mechanical anomalies, achieving a classification accuracy of 96.38%. The framework achieves significant accuracy improvement over baseline methods while maintaining computational efficiency, validated through comprehensive mechanical fault simulations. This parameter-adaptive methodology demonstrates enhanced stability in signal decomposition and improved temporal feature discernment, proving particularly effective in handling non-stationary vibration signals under real operational conditions. The results establish practical viability for industrial condition monitoring applications through robust feature extraction and reliable fault pattern recognition. Full article

A simple, fast, and low-cost pre-concentration methodology based on the application of multi-template molecularly imprinted polymers (mt-MIP) in a solid-phase extraction system coupled with capillary electrophoresis was developed for the determination of naproxen, diclofenac, and ibuprofen in environmental water samples. A systematic study of the mt-MIP composition was conducted using a second-order simplex lattice experiment design (fraction of the functional monomer methacrylic acid (MAA), the total moles of functional monomers, and the total moles of the cross-linker agent). The optimal mt-MIP, consisting of 0.025 mmol of each analyte, with 2.40 mmol of methacrylic acid (MAA) and 3.60 mmol of 4-vinylpyridine (4VP) and 23.00 mmol of the cross-linker agent (EGDMA), was coupled to an SPE system under the optimal conditions: pH = 3.5; 20 mg of mt-MIP; and an eluent (MeOH/NaOH [0.001]). This methodology provides limits of detection from 3.00 to 12.00 µg L⁻¹ for the studied NSAIDs. The methodology’s precision was evaluated in terms of inter- and intra-day repeatability, with %RSD < 10% in all cases. Finally, the proposed method can be successfully applied in the analysis of environmental water samples (bottle, tap, cistern, well, and river water samples), which demonstrates the developed method’s robustness. Full article

Time-to-Digital Converters (TDCs) implemented on Field-Programmable Gate Arrays (FPGAs) have become increasingly prevalent across a wide range of scientific and engineering disciplines, such as high-energy physics experiments, autonomous driving, robotic navigation, and medical imaging, owing to their cost-effectiveness, high precision, and rapid development cycles. This article presents a 3-tap heterogeneous tapped delay-line (TDL) architecture for a FPGA-based TDC that can be employed for multi-channel time-of-flight measurement. The TDC desgin is based on the open-source jTDC, featuring single-cycle dead time and multi-channel expansion capabilities, with an original precision of 30 ps. Combined with jTDC’s dynamic caching mechanism using dual-page memory, this work employs a dual-cycle encoding and calibration. The proposed architecture has been implemented on a Xilinx Artix-7 FPGA. According to the experimental results, an optimal 3-tap heterogeneous TDL architecture achieves a resolution of 23.220 ps and a typical precision of 17.520 ps, whereas an optimal 4-tap heterogeneous TDL architecture demonstrates a resolution of 17.530 ps and a typical precision of 17.213 ps. A comparison with recently published state-of-the-art FPGA-based TDCs is provided at the end of the article. Full article

In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the distribution network structure and the extensive application of energy storage technology, the active distribution network has evolved into a more flexible and interactive “source–grid–load–storage” diversified structure. When electric vehicles are plugged into charging piles for charging and discharging, it inevitably exerts a significant impact on the control and operation of the power grid. Therefore, in the context of the extensive integration of electric vehicles, delving into the charging and discharging behaviors of electric vehicle clusters and integrating them into the optimization of the active distribution network holds great significance for ensuring the safe and economic operation of the power grid. This paper adopts the two-stage “constant-current and constant-voltage” charging mode, which has the least impact on battery life, and classifies the electric vehicle cluster into basic EV load and controllable EV load. The controllable EV load is regarded as a special “energy storage” resource, and a corresponding model is established to enable its participation in the coordinated control of the active distribution network. Based on the optimization and control of the output behaviors of gas turbines, flexible loads, energy storage, and electric vehicle clusters, this paper proposes a two-layer coordinated control model for the scheduling layer and network layer of the active distribution network and employs the improved multi-target beetle antennae search optimization algorithm (MTTA) in conjunction with the Cplex solver for solution. Through case analysis, the results demonstrate that the “source–grid–load–storage” coordinated control of the active distribution network can fully tap the potential of resources such as flexible loads on the “load” side, traditional energy storage, and controllable EV clusters; realize the economic operation of the active distribution network; reduce load and voltage fluctuations; and enhance power quality. Full article

With the increasing penetration of distributed photovoltaics (PVs) in distribution networks (DNs), issues like voltage violations and fluctuations are becoming more prominent. This paper proposes a spatio-temporal adaptive voltage coordination control strategy involving multiple timescales and multi-device collaboration. Aiming at the heavy workload caused by the continuous sampling of real-time data in the whole domain, an intra-day innovative construction of intra-day minute-level optimization and real-time adaptive control double-layer control mode are introduced. Intra-day minute-level refinement of on-load tap changer (OLTC) and step voltage regulator (SVR) day-ahead scheduling plans to fully utilize OLTC and SVR voltage regulation capabilities and improve voltage quality is discussed. In real-time adaptive control, a regional autonomy mechanism based on the functional area voltage quality risk prognostication coefficient (VQRPC) is innovatively proposed, where each functional area intelligently selects the time period for real-time voltage regulation of distributed battery energy storage systems (DESSs) based on VQRPC value, in order to improve real-time voltage quality while reducing the data sampling workload. Aiming at the state of charge (SOC) management of DESS, a novel functional area DESS available capacity management mechanism is proposed to coordinate DESS output and improve SOC homogenization through dynamically updated power–capacity availability (PCA). And vine model threshold band (VMTB) and deviation optimization management (DOM) strategies based on functional area are innovatively proposed, where DOM optimizes DESS output through the VMTB to achieve voltage fluctuation suppression while optimizing DESS available capacity. Finally, the DESS and electric vehicle (EV) cooperative voltage regulation mechanism is constructed to optimize DESS capacity allocation, and the black-winged kite algorithm (BKA) is used to manage DESS output. The results of a simulation on a modified IEEE 33 system show that the proposed strategy reduces the voltage fluctuation rate of each functional area by an average of 36.49%, reduces the amount of data collection by an average of 68.31%, and increases the available capacity of DESS by 5.8%, under the premise of a 100% voltage qualification rate. Full article

The intensification of economic globalization and the growing scarcity of global land resources have magnified the complexity of future land use and land cover (LULC) changes. In Mainland Southeast Asia (MSEA), these transformations are particularly pronounced, yet comprehensive, targeted, and systematic reviews are scant. This research employs bibliometrics and critical literature review methodologies to scrutinize 1956 relevant publications spanning from 1990–2023, revealing key insights into the contributors to land use studies in MSEA, which include not only local researchers from countries like Thailand and Vietnam but also international scholars from the United States, China, Japan, and France. Despite this, the potential for global collaboration has not been fully tapped. This study also notes a significant evolution in data analysis methods, transitioning from reliance on single data sources to employing sophisticated multi-source data fusion, from manual feature extraction to leveraging automated deep learning processes, and from simple temporal change detection to comprehensive time series analysis using tools like Google Earth Engine (GEE). This shift encompasses the progression from small-scale case studies to extensive multi-scale system analyses employing advanced spatial statistical models and integrated technologies. Moreover, the thematic emphasis of research has evolved markedly, transitioning from traditional practices like slash-and-burn agriculture and deforestation logging to the dynamic monitoring of specialized tree species such as rubber plantations and mangroves. Throughout this period, there has been a growing focus on the broad environmental impacts of land cover change, encompassing soil degradation, carbon storage, climate change responses, ecosystem services, and biodiversity. This research not only offers a comprehensive understanding of the LULC research landscape in MSEA but also provides critical scientific references that can inform future policy-making and land management strategies. Full article

Miniscrews are used in orthodontic treatment and can be applied immediately after implantation, making their initial stability crucial. However, clinical reports show that the success rate is not 100%, and many researchers have tried to identify the factors influencing success and optimize designs. A review of the literature reveals that studies on the same geometric parameter of miniscrews using different indicators and different brand samples have led to conflicting results. This study will use consistent miniscrew conditions to verify whether the design differences in the literature are reasonable. This study employs the Taguchi method and ANOVA for optimization analysis. The four control factors comprise thread pitch, thread depth, tip taper angle, and self-tapping notch. Using an L9(3⁴) orthogonal array, the experimental models are reduced to nine. The primary stability indicators for the miniscrew include bending strength, pull-out strength, insertion torque, and self-tapping performance. The results of the single-objective experiments in this study align with the findings from the other literature. However, when analyzed collectively, they do not yield the same optimal solution. Under equal weighting, the combined multi-objective optimal solution is A2B2C1D1. This study exhibits minimal experimental error, ensuring high analytical reliability. The findings confirm that the optimal design does not converge across four single-objective analyses, as different stability indicators yield contradictory trends in design parameters. Given that these four indicators already demonstrate notable discrepancies, the influence of additional stability factors would be even more pronounced. Therefore, a multi-objective optimization approach is essential for the rational design of miniscrews. Full article